Using floating catchment area (FCA) metrics to predict health care utilization patterns

Area of Science:

• Health Services Research
• Spatial Analysis
• Geographic Information Systems (GIS)

Background:

• Floating Catchment Area (FCA) metrics assess healthcare access and identify disparities.
• The predictive power of FCA metrics for actual patient utilization patterns remains under-explored.
• This study investigates FCA metrics' utility in forecasting patient flow from residences to healthcare facilities.

Purpose of the Study:

• To evaluate the effectiveness of FCA metrics in predicting patient healthcare utilization patterns.
• To compare FCA metrics against traditional accessibility measures for predicting patient flow.
• To determine the sensitivity of FCA metrics to distance decay function parameters.

Main Methods:

• Utilized over one million inpatient hospital visits in Michigan.
• Calculated expected utilization patterns using four FCA metrics and two traditional metrics (distance, Huff model).
• Conducted a sensitivity analysis on distance decay functions and parameters to assess prediction accuracy.

Main Results:

• Three Step FCA (3SFCA) and Modified Two Step FCA (M2SFCA) demonstrated the highest accuracy, predicting nearly 74% of hospital visits correctly.
• These two FCA metrics exhibited the lowest sensitivity to variations in distance decay functions and parameters.
• Both FCA metrics outperformed traditional distance and Huff models in predicting utilization.

Conclusions:

• FCA metrics provide reliable predictions of patient healthcare utilization patterns.
• FCA-based utilization models can serve as a viable alternative when direct utilization data is unavailable.
• This research validates the application of FCA metrics beyond access assessment to predicting actual healthcare-seeking behavior.